code: def set_batch_size(args):
      """Fixes the total batch_size to be agnostic to the GPU count."""
      if args.fix_batch_size is not None:
          args.update_freq = [
              math.ceil(
                  batch_size
                  / (args.max_sentences * args.distributed_world_size)
              )
              for batch_size in args.fix_batch_size
          ]
          print(
              "Training on %d GPUs, max_sentences=%d, update_freq=%s"
              % (
                  args.distributed_world_size,
                  args.max_sentences,
                  repr(args.update_freq),
              )
          )
  
file_path: ray/doc/source/ray-core/examples/lm/ray_train.py
imports: import copy
  import math
  import socket
  import time
  from contextlib import closing
  import fairseq
  from fairseq import options
  from fairseq_cli.train import main
  import ray
label_desc: Use image node_id 4 for calling a global function with example usage: set_batch_size(args) without return types